Mouse Tumor analysis

Only tumor cells

st <- str_detect(colnames(countsMgfpTumor), "^s")
cObjSng <- spCounts(countsMgfpTumor[, st], countsMgfpTumorERCC[, st])

plotUnsupervisedClass(uObj_tumor, cObjSng)

plotUnsupervisedMarkers(
  uObj_tumor, cObjSng,
  c("Lgr5", "Muc2", "Ptprc", "Chga", "Lyz1", "Dclk1", "Slc40a1"), 
  pal = RColorBrewer::brewer.pal(8, "Set1")
)

#wont plot Alpi; error; why?

sn <- str_detect(colnames(countsMgfp), "^s")
commonGenes <- intersect(rownames(countsMgfp), rownames(countsRegev))
commonGenes <- intersect(commonGenes, rownames(countsMgfpTumor))

sng <- cbind(countsMgfp[commonGenes, sn], countsMgfpTumor[commonGenes, st], countsRegev[commonGenes, ])
mul <- cbind(countsMgfp[commonGenes, !sn], countsMgfpTumor[commonGenes, !st])

erccSng <- cbind(
  countsMgfpERCC[, sn],
  countsMgfpTumorERCC[, st],
  matrix(NA, nrow = nrow(countsMgfpERCC), ncol = ncol(countsRegev))
)
erccMul <- cbind(countsMgfpERCC[, !sn], countsMgfpTumorERCC[, !st])

#setup spCounts
cObjSng <- spCounts(sng, erccSng)

## is.na(c(counts, counts.ercc) returned TRUE

cObjMul <- spCounts(mul, erccMul)

All cells

plotUnsupervisedClass(uObj_all, cObjSng)

plotUnsupervisedMarkers(
  uObj_all, cObjSng,
  c("Lgr5", "Muc2", "Ptprc", "Chga", "Alpi", "Lyz1", "Dclk1", "Slc40a1"), 
  pal = RColorBrewer::brewer.pal(8, "Set1")
)

plotUnsupervisedClass(uObj_all, cObjSng) %>%
  plotData() %>%
  mutate(type = if_else(Sample %in% countsMgfpTumorMeta$sample, "Tumor", "Normal")) %>%
  ggplot() +
  geom_point(aes(`t-SNE dim 1`, `t-SNE dim 2`, colour = type)) +
  theme_few()

More blood markers

t-cell: Cd4¹
b-cell: Cd19¹ dendritic cell: Cd11c (Itgax)¹
NK cell: Tbx21²
Stem/precursor: Cd34¹
Macrophage: Cd11b (Itgam)¹
Granulocyte: Arg2³
Platelet: Cd9¹
Erythrocyte: Rhag⁴

1. https://www.bdbiosciences.com/documents/cd_marker_handbook.pdf
   
2. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3572860/
   
3. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4184787/
   
4. https://www.bdbiosciences.com/documents/BD_Reagents_CDMarkerMouse_Poster.pdf

#https://www.bdbiosciences.com/documents/cd_marker_handbook.pdf
#t-cell: CD3, CD4, CD8
#b-cell: CD19
#dendritic cell: CD11c (Itgax), CD123 (Il3ra)
#NK cell: Arsb, Tbx21, Ifng (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3572860/)
#Stem/precursor: CD34
#Macrophage: CD11b (Itgam), Ly-71
#Granulocyte: Ceacam10, Arg2 (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4184787/)
#Platelet: CD41, CD9
#Erythrocyte: Rhag (https://www.bdbiosciences.com/documents/BD_Reagents_CDMarkerMouse_Poster.pdf)
#Endothelial Cell: CD106, CD31
#Epithelial Cell: CD326 (EPCAM1)

plotUnsupervisedMarkers(
  uObj_all, cObjSng,
  c("Cd4", "Cd19", "Tbx21", "Cd34", "Itgam", "Arg2", "Cd9", "Rhag"), 
  pal = RColorBrewer::brewer.pal(8, "Set1")
)

Classification from fig 1. overlayed on fig 2.

plotUnsupervisedClass(uObj_all, cObjSng) %>%
  plotData() %>%
  full_join(tibble(Sample = rownames(getData(uObj_tumor, "tsne")), class = getData(uObj_tumor, "classification"))) %>%
  ggplot() +
  geom_point(aes(`t-SNE dim 1`, `t-SNE dim 2`, colour = class)) +
  theme_few()

## Joining, by = "Sample"